


'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def 	a__		(     self							)    ->  str:
					lowercase						:		List[Any]   					=		tempfile.mkdtemp()

					lowercase						:		Tuple   					=		BlipImageProcessor()
					lowercase						:		List[str]   					=		BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"							)

					lowercase						:		List[Any]   					=		BlipProcessor(_lowercase							,      _lowercase							)

					processor.save_pretrained(self.tmpdirname							)

				def 	a__		(     self							,      **a_							)    ->  int:
					return AutoProcessor.from_pretrained(self.tmpdirname							,      **_lowercase							).tokenizer

				def 	a__		(     self							,      **a_							)    ->  Optional[int]:
					return AutoProcessor.from_pretrained(self.tmpdirname							,      **_lowercase							).image_processor

				def 	a__		(     self							)    ->  List[Any]:
					shutil.rmtree(self.tmpdirname							)

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		List[Any]   					=		[np.random.randint(2_5_5							,      size=(3, 3_0, 4_0_0)							,      dtype=np.uinta							)]

					lowercase						:		int   					=		[Image.fromarray(np.moveaxis(_lowercase							,      0							,      -1							)							) for x in image_inputs]

					return image_inputs

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Union[str, Any]   					=		BlipProcessor(tokenizer=self.get_tokenizer()							,      image_processor=self.get_image_processor()							)
					processor.save_pretrained(self.tmpdirname							)

					lowercase						:		Optional[int]   					=		self.get_tokenizer(bos_token="(BOS)"							,      eos_token="(EOS)"							)
					lowercase						:		int   					=		self.get_image_processor(do_normalize=_lowercase							,      padding_value=1.0							)

					lowercase						:		List[str]   					=		BlipProcessor.from_pretrained(
					    self.tmpdirname							,      bos_token="(BOS)"							,      eos_token="(EOS)"							,      do_normalize=_lowercase							,      padding_value=1.0							)

					self.assertEqual(processor.tokenizer.get_vocab()							,      tokenizer_add_kwargs.get_vocab()							)
					self.assertIsInstance(processor.tokenizer							,      _lowercase							)

					self.assertEqual(processor.image_processor.to_json_string()							,      image_processor_add_kwargs.to_json_string()							)
					self.assertIsInstance(processor.image_processor							,      _lowercase							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		List[str]   					=		self.get_image_processor()
					lowercase						:		Dict   					=		self.get_tokenizer()

					lowercase						:		List[Any]   					=		BlipProcessor(tokenizer=_lowercase							,      image_processor=_lowercase							)

					lowercase						:		Optional[Any]   					=		self.prepare_image_inputs()

					lowercase						:		Optional[Any]   					=		image_processor(_lowercase							,      return_tensors="np"							)
					lowercase						:		Any   					=		processor(images=_lowercase							,      return_tensors="np"							)

					for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()							,      input_processor[key].sum()							,      delta=1e-2							)

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		List[Any]   					=		self.get_image_processor()
					lowercase						:		Dict   					=		self.get_tokenizer()

					lowercase						:		List[str]   					=		BlipProcessor(tokenizer=_lowercase							,      image_processor=_lowercase							)

					lowercase						:		List[Any]   					=		"""lower newer"""

					lowercase						:		Any   					=		processor(text=_lowercase							)

					lowercase						:		Union[str, Any]   					=		tokenizer(_lowercase							,      return_token_type_ids=_lowercase							)

					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]							,      encoded_processor[key]							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Union[str, Any]   					=		self.get_image_processor()
					lowercase						:		int   					=		self.get_tokenizer()

					lowercase						:		Optional[Any]   					=		BlipProcessor(tokenizer=_lowercase							,      image_processor=_lowercase							)

					lowercase						:		List[Any]   					=		"""lower newer"""
					lowercase						:		Optional[Any]   					=		self.prepare_image_inputs()

					lowercase						:		int   					=		processor(text=_lowercase							,      images=_lowercase							)

					self.assertListEqual(list(inputs.keys()							)							,      ["pixel_values", "input_ids", "attention_mask"]							)

					# test if it raises when no input is passed
					with pytest.raises(_lowercase							):
						processor()

				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Union[str, Any]   					=		self.get_image_processor()
					lowercase						:		List[Any]   					=		self.get_tokenizer()

					lowercase						:		Union[str, Any]   					=		BlipProcessor(tokenizer=_lowercase							,      image_processor=_lowercase							)

					lowercase						:		int   					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					lowercase						:		Optional[Any]   					=		processor.batch_decode(_lowercase							)
					lowercase						:		Optional[Any]   					=		tokenizer.batch_decode(_lowercase							)

					self.assertListEqual(_lowercase							,      _lowercase							)


				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Optional[Any]   					=		self.get_image_processor()
					lowercase						:		int   					=		self.get_tokenizer()

					lowercase						:		List[Any]   					=		BlipProcessor(tokenizer=_lowercase							,      image_processor=_lowercase							)

					lowercase						:		int   					=		"""lower newer"""
					lowercase						:		List[str]   					=		self.prepare_image_inputs()

					lowercase						:		Any   					=		processor(text=_lowercase							,      images=_lowercase							)

					# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
					self.assertListEqual(list(inputs.keys()							)							,      ["pixel_values", "input_ids", "attention_mask"]							)





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case				:      Optional[Any]   		=			10


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			for i in range(a__  ,	a__):
						if array[i] == target:
									return i
			return -1


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Any 							= 0
			a_						:						List[str] 							= len(a__)
			while left <= right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)

						a_						:						Dict 							= (left + right) // 3 + 1
						a_						:						Optional[Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									a_						:						int 							= one_third - 1
						elif array[two_third] < target:
									a_						:						Any 							= two_third + 1

						else:
									a_						:						Optional[int] 							= one_third + 1
									a_						:						int 							= two_third - 1
			else:
						return -1


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			if left < right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)
						a_						:						Optional[int] 							= (left + right) // 3 + 1
						a_						:						Union[str, Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									return rec_ternary_search(a__  ,	one_third - 1  ,	a__  ,	a__)
						elif array[two_third] < target:
									return rec_ternary_search(two_third + 1  ,	a__  ,	a__  ,	a__)
						else:
									return rec_ternary_search(one_third + 1  ,	two_third - 1  ,	a__  ,	a__)
			else:
						return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__snake_case				:      Optional[int]   		=			input("""Enter numbers separated by comma:\n""").strip()
	__snake_case				:      Optional[int]   		=			[int(item.strip()) for item in user_input.split(""",""")]
	assert collection == sorted(collection), F"List must be ordered.\n{collection}."
	__snake_case				:      int   		=			int(input("""Enter the number to be found in the list:\n""").strip())
	__snake_case				:      List[Any]   		=			ite_ternary_search(collection, target)
	__snake_case				:      Optional[int]   		=			rec_ternary_search(0, len(collection) - 1, collection, target)
	if resulta != -1:
		print(F"""Iterative search: {target} found at positions: {resulta}""")
		print(F"""Recursive search: {target} found at positions: {resulta}""")
	else:
		print("""Not found""")


